Kevin Kasa

I am an MASc candidate at the University of Guelph, where I am advised by Graham Taylor.

I graduated with a B.Eng in Aerospace Engineering from the Toronto Metropolitan University (previously Ryerson University). Previously, I've worked on applying machine learning to medical imaging and surgical assessment with the Orthopaedic Biomechanics Lab at the Sunnybrook Research Institute. I also spent one year working on machine learning for Geointelligence applications at MDA. In the past I've worked on research projects including robotics, orbital dynamics, synthetic aperture radar (SAR) and remote sensing, as well as at companies like IBM and Deloitte.

In 2023, I joined the Office of the Chief Science Advisor's Youth Council

In addition to machine learning research, I am also very interested in deep tech entrepreneurship, having previously worked on bringing autonomous high-payload drones to the skies.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

Research

I'm interested in computer vision and machine learning.

Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data

Large-scale evaluations demonstrating the challenges of utilizing conformal prediction in distribution shift or long-tailed data regimes.

Multi-Modal Deep Learning for Assessing Surgeon Technical Skill

Multi-modal (kinematic + image) deep learning model for surgical skill assessment.

Deep Learning for Vessel Detection and Identification from Spaceborn Optical Imagery

Siamese networks for re-identifying vessels via spaceborn optical imagery. Developed an automated pipeline for collecting a large geospatial dataset (WorldView-2/3), processing, & detecting 200k+ vessels.

A modular system for behaviour analytics from airborne full-motion video

Object detection + tracking from UAS video.

Design and source code from Jon Barron's website